Busting performance issues, AI edition
The article discusses the use of AI coding agents to identify and resolve performance issues in codebases. It highlights a case study involving the bytecode library, a Python tool for bytecode manipulation. The focus is on the methods and tools used rather than comparing different AI models for performance improvement.
- ▪AI coding agents have advanced to effectively understand and modify codebases.
- ▪Benchmarks show a significant gap between synthetic performance and actual project performance.
- ▪The article explores how a coding agent can improve performance using focused profiling data.
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Opening excerpt (first ~120 words) tap to expand
Table of contents: Enter AI bytecode: Our Case Study A Bit of Setup Required Establishing the Baseline Communicating Our Intent to the Agent On a Mission A Guided Tour of Profiles A Bonus: Learning to Read Profiles Is Profiling Data Really Necessary? The Takeaway In a previous post we looked at how to find and resolve performance issues lurking in any codebase by simply running some of its code through a profiler. The simplest thing we can do, especially when we come to a new codebase that is totally unknown to us is to run its test suite (or a subset of it) and see what code paths are being exercised.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Github.
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